# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import math
from collections.abc import Iterable, Mapping, Sequence
from dataclasses import dataclass, fields
from functools import cached_property
from typing import Literal, Optional, TypedDict, Union

import torch
import torch.nn as nn
import torch.nn.functional as F
from mistral_common.protocol.instruct.messages import (ImageChunk, TextChunk,
                                                       UserMessage)
from mistral_common.protocol.instruct.request import ChatCompletionRequest
from mistral_common.tokens.tokenizers.multimodal import ImageEncoder
from PIL import Image
from transformers import PixtralVisionConfig, TensorType
from transformers.image_utils import ImageInput
from transformers.models.pixtral.image_processing_pixtral import (
    _num_image_tokens as _get_pixtral_hf_num_image_tokens)
from transformers.models.pixtral.modeling_pixtral import (
    PixtralRotaryEmbedding, apply_rotary_pos_emb, position_ids_in_meshgrid)
from transformers.tokenization_utils_base import TextInput

from vllm.config import VllmConfig
from vllm.distributed import divide, get_tensor_model_parallel_world_size
from vllm.model_executor.layers.activation import get_act_and_mul_fn
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
                                               QKVParallelLinear,
                                               RowParallelLinear)
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalKwargsItems
from vllm.multimodal.inputs import (MultiModalDataDict, MultiModalFieldConfig,
                                    NestedTensors)
from vllm.multimodal.parse import (ImageProcessorItems, ImageSize,
                                   MultiModalDataItems)
from vllm.multimodal.processing import (BaseMultiModalProcessor,
                                        BaseProcessingInfo,
                                        MultiModalProcessingInfo,
                                        PromptReplacement, PromptUpdate,
                                        PromptUpdateDetails)
from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
from vllm.platforms import current_platform
from vllm.sequence import IntermediateTensors
from vllm.transformers_utils.tokenizer import (MistralTokenizer,
                                               cached_tokenizer_from_config)

from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
from .utils import (flatten_bn, init_vllm_registered_model, maybe_prefix,
                    merge_multimodal_embeddings)
from .vision import VisionEncoderInfo, resolve_visual_encoder_outputs

try:
    from xformers import ops as xops
    if (current_platform.is_cuda()
            and current_platform.has_device_capability(100)):
        # Xformers FA is not compatible with B200
        USE_XFORMERS_OPS = False
    else:
        USE_XFORMERS_OPS = True
except ImportError:
    USE_XFORMERS_OPS = False

PATCH_MERGE = "patch_merge"


class PixtralImagePixelInputs(TypedDict):
    type: Literal["pixel_values"]

    images: Union[torch.Tensor, list[torch.Tensor]]
    """
    Shape: `(batch_size * num_images, num_channels, image_width, image_height)`

    The result of stacking `ImageEncoding.tokens` from each prompt.
    """


class PixtralProcessorAdapter:
    """
    Provide a HF-compatible interface for
    `mistral_common.tokens.tokenizers.multimodal.ImageEncoder`.
    """

    def __init__(self, tokenizer: MistralTokenizer) -> None:
        super().__init__()

        self.tokenizer = tokenizer

    @property
    def image_processor(self) -> ImageEncoder:
        image_encoder = self.tokenizer.instruct.mm_encoder
        assert isinstance(image_encoder, ImageEncoder)
        return image_encoder

    @cached_property
    def image_break_id(self) -> int:
        return self.image_processor.special_ids.img_break

    @cached_property
    def image_token_id(self) -> int:
        return self.image_processor.special_ids.img

    @cached_property
    def image_end_id(self) -> int:
        return self.image_processor.special_ids.img_end

    @cached_property
    def image_size(self) -> int:
        return self.image_processor.mm_config.max_image_size

    @cached_property
    def patch_size(self) -> int:
        return self.image_processor.mm_config.image_patch_size

    def __call__(
        self,
        text: Optional[Union[TextInput, list[TextInput]]] = None,
        images: Optional[Union[ImageInput, list[ImageInput]]] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        **kwargs,
    ) -> Mapping[str, NestedTensors]:
        if text is None:
            text = []
        if not isinstance(text, list):
            text = [text]
        if images is None:
            images = []
        if not isinstance(images, list):
            images = [images]

        if not images:
            input_ids = self.tokenizer(text).input_ids

            return {"input_ids": torch.tensor(input_ids)}

        # Allow dummy text, which is used for profiling as well as token inputs
        if any(len(t) > 0 for t in text):
            raise ValueError(
                "You've passed text inputs instead of token inputs. "
                "Make sure to process your input via `mistral_common`'s "
                "tokenizer or pass a chat completion request. "
                "For more info, see: "
                "https://github.com/vllm-project/vllm/issues/8411.")

        images_processed = list[torch.Tensor]()
        images_tokens = list[torch.Tensor]()

        for image in images:
            image_inputs = self.image_processor(ImageChunk(image=image))
            image_processed = torch.tensor(image_inputs.image)
            image_tokens = torch.tensor(image_inputs.tokens)

            images_processed.append(image_processed)
            images_tokens.append(image_tokens)

        return {
            "input_ids": torch.cat(images_tokens)[None].expand(len(text), -1),
            "images": images_processed,
        }


class PixtralProcessingInfo(BaseProcessingInfo):

    def get_tokenizer(self) -> MistralTokenizer:
        tokenizer = cached_tokenizer_from_config(self.ctx.model_config)
        if not isinstance(tokenizer, MistralTokenizer):
            raise ValueError("This model requires `--tokenizer-mode mistral`")

        return tokenizer

    def get_hf_processor(self) -> PixtralProcessorAdapter:
        return PixtralProcessorAdapter(self.get_tokenizer())

    def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
        return {"image": None}

    def get_vision_config(
        self,
        processor: Optional[PixtralProcessorAdapter] = None,
    ):
        if processor is None:
            processor = self.get_hf_processor()

        return PixtralVisionConfig(
            image_size=processor.image_size,
            patch_size=processor.patch_size,
        )

    def get_num_image_tokens(
        self,
        *,
        image_width: int,
        image_height: int,
        processor: Optional[PixtralProcessorAdapter] = None,
    ) -> int:
        if processor is None:
            processor = self.get_hf_processor()

        ncols, nrows = processor.image_processor._image_to_num_tokens(
            Image.new("RGB", (image_width, image_height)))

        return ncols * nrows

    def get_image_size_with_most_features(self) -> ImageSize:
        image_processor = self.get_hf_processor().image_processor
        max_image_size = image_processor.mm_config.max_image_size

        return ImageSize(width=max_image_size, height=max_image_size)


class PixtralDummyInputsBuilder(BaseDummyInputsBuilder[PixtralProcessingInfo]):

    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
        return ""

    def get_dummy_mm_data(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
    ) -> MultiModalDataDict:
        num_images = mm_counts.get("image", 0)

        target_width, target_height = \
            self.info.get_image_size_with_most_features()

        return {
            "image":
            self._get_dummy_images(width=target_width,
                                   height=target_height,
                                   num_images=num_images)
        }

    def get_dummy_processor_inputs(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
    ) -> ProcessorInputs:
        tokenizer = self.info.get_tokenizer()

        dummy_text = self.get_dummy_text(mm_counts)
        dummy_mm_data = self.get_dummy_mm_data(seq_len, mm_counts)
        dummy_images = dummy_mm_data.get("image", [])
        tokenization_kwargs = {"truncation": False}

        request = ChatCompletionRequest(messages=[
            UserMessage(content=[
                TextChunk(text=dummy_text),
                *(ImageChunk(image=image) for image in dummy_images),
            ]),
        ])
        res = tokenizer.mistral.encode_chat_completion(request)
        dummy_tokens = res.tokens

        return ProcessorInputs(prompt=dummy_tokens,
                               mm_data=dummy_mm_data,
                               tokenization_kwargs=tokenization_kwargs)


class PixtralMultiModalProcessor(BaseMultiModalProcessor[PixtralProcessingInfo]
                                 ):

    def _get_mm_fields_config(
        self,
        hf_inputs: Mapping[str, NestedTensors],
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
        return dict(images=MultiModalFieldConfig.batched("image"))

    def _get_prompt_updates(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
        out_mm_kwargs: MultiModalKwargsItems,
    ) -> Sequence[PromptUpdate]:
        processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)

        image_break_id = processor.image_break_id
        image_token_id = processor.image_token_id
        image_end_id = processor.image_end_id

        def get_replacement(item_idx: int):
            images = mm_items.get_items("image", ImageProcessorItems)
            image_size = images.get_image_size(item_idx)

            ncols, nrows = processor.image_processor._image_to_num_tokens(
                Image.new("RGB", (image_size.width, image_size.height)))

            tokens = ([image_token_id] * ncols + [image_break_id]) * nrows
            tokens[-1] = image_end_id

            return PromptUpdateDetails.select_token_id(tokens, image_token_id)

        return [
            PromptReplacement(
                modality="image",
                target="",  # Never match the prompt (see below note)
                replacement=get_replacement,
            ),
        ]

    def _cached_apply_hf_processor(
        self,
        prompt: Union[str, list[int]],
        mm_data_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
        tokenization_kwargs: Mapping[str, object],
        *,
        return_mm_hashes: bool,
    ) -> tuple[list[int], MultiModalProcessingInfo, bool]:
        prompt_ids, mm_info, _ = super()._cached_apply_hf_processor(
            prompt=prompt,
            mm_data_items=mm_data_items,
            hf_processor_mm_kwargs=hf_processor_mm_kwargs,
            tokenization_kwargs=tokenization_kwargs,
            return_mm_hashes=return_mm_hashes,
        )

        # NOTE: The tokens are already inserted by the chat template
        return prompt_ids, mm_info, True


@MULTIMODAL_REGISTRY.register_processor(PixtralMultiModalProcessor,
                                        info=PixtralProcessingInfo,
                                        dummy_inputs=PixtralDummyInputsBuilder)
class PixtralForConditionalGeneration(nn.Module, SupportsMultiModal,
                                      SupportsPP):

    @classmethod
    def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]:
        if modality.startswith("image"):
            return None

        raise ValueError("Only image modality is supported")

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        config = vllm_config.model_config.hf_config
        multimodal_config = vllm_config.model_config.multimodal_config
        self.config = config
        self.multimodal_config = multimodal_config

        dataclass_fields = {field.name for field in fields(VisionEncoderArgs)}
        vision_args = {
            key: value
            for key, value in self.config.vision_config.to_dict().items()
            if key in dataclass_fields
        }

        self.vision_args = VisionEncoderArgs(**vision_args)

        # init MistralForCausalLM
        self.language_model = init_vllm_registered_model(
            vllm_config=vllm_config,
            hf_config=config.text_config,
            prefix=maybe_prefix(prefix, "language_model"),
        )

        self.vision_encoder = VisionTransformer(self.vision_args)

        if self.vision_args.add_pre_mm_projector_layer_norm:
            self.pre_mm_projector_norm = RMSNorm(self.vision_args.hidden_size,
                                                 eps=1e-5)

        if self.vision_args.mm_projector_id == PATCH_MERGE:
            self.patch_merger = PatchMerger(
                vision_encoder_dim=self.vision_args.hidden_size,
                spatial_merge_size=self.vision_args.spatial_merge_size,
                use_mlp_bias=False,
            )

        self.vision_language_adapter = VisionLanguageAdapter(
            self.vision_args, dim=config.text_config.hidden_size)

        self.make_empty_intermediate_tensors = (
            self.language_model.make_empty_intermediate_tensors)

    def _parse_and_validate_image_input(
            self, **kwargs: object) -> Optional[PixtralImagePixelInputs]:
        images = kwargs.pop("images", None)
        if images is None:
            return None

        if not isinstance(images, (torch.Tensor, list)):
            raise ValueError("Incorrect type of images. "
                             f"Got type: {type(images)}")

        return PixtralImagePixelInputs(
            type="pixel_values",
            images=flatten_bn(images),
        )

    def _process_image_input(
        self,
        image_input: PixtralImagePixelInputs,
    ) -> tuple[torch.Tensor, ...]:
        images = image_input["images"]
        image_features = self.vision_encoder(images)
        feature_sizes = [
            image_feature.shape[0] for image_feature in image_features
        ]
        image_features = torch.cat(image_features)
        if self.vision_args.add_pre_mm_projector_layer_norm:
            image_features = self.pre_mm_projector_norm(image_features)
        if self.vision_args.mm_projector_id == PATCH_MERGE:
            patch_size = self.vision_args.patch_size
            spatial_merge_size_square = self.vision_args.spatial_merge_size**2
            img_patch_dims = [(img.shape[1] // patch_size,
                               img.shape[2] // patch_size) for img in images]
            feature_sizes = [
                feature_size // spatial_merge_size_square
                for feature_size in feature_sizes
            ]
            image_features = self.patch_merger(image_features,
                                               image_sizes=img_patch_dims)
        image_embeds = self.vision_language_adapter(image_features)
        image_embeds = torch.split(image_embeds, feature_sizes)
        return image_embeds

    def get_language_model(self) -> torch.nn.Module:
        return self.language_model

    def get_multimodal_embeddings(self,
                                  **kwargs: object) -> MultiModalEmbeddings:
        image_input = self._parse_and_validate_image_input(**kwargs)
        if image_input is None:
            return []

        return self._process_image_input(image_input)

    def get_input_embeddings(
        self,
        input_ids: torch.Tensor,
        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
    ) -> torch.Tensor:
        inputs_embeds = self.language_model.get_input_embeddings(input_ids)
        if multimodal_embeddings is not None \
            and len(multimodal_embeddings) != 0:
            inputs_embeds = merge_multimodal_embeddings(
                input_ids,
                inputs_embeds,
                multimodal_embeddings,
                self.vision_args.image_token_id,
            )
        return inputs_embeds

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        intermediate_tensors: Optional[IntermediateTensors] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        **kwargs: object,
    ) -> Union[torch.Tensor, IntermediateTensors]:
        """Run forward pass for pixtral."""
        if intermediate_tensors is not None:
            inputs_embeds = None

        # NOTE: In v1, inputs_embeds is always generated at model runner, this
        # condition is for v0 compatibility.
        elif inputs_embeds is None:
            vision_embeddings = self.get_multimodal_embeddings(**kwargs)
            inputs_embeds = self.get_input_embeddings(input_ids,
                                                      vision_embeddings)
            input_ids = None

        hidden_states = self.language_model.model(input_ids,
                                                  positions,
                                                  intermediate_tensors,
                                                  inputs_embeds=inputs_embeds)

        return hidden_states

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
        sampling_metadata: SamplingMetadata,
    ) -> Optional[torch.Tensor]:
        return self.language_model.compute_logits(hidden_states,
                                                  sampling_metadata)

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):

        def is_vision_encoder_weights(weight: tuple[str, torch.Tensor]):
            return weight[0].startswith("vision_encoder")

        def is_vision_lang_adapter_weights(weight: tuple[str, torch.Tensor]):
            return weight[0].startswith("vision_language_adapter")

        def is_patch_merger(weight: tuple[str, torch.Tensor]):
            return weight[0].startswith("patch_merger")

        def is_pre_mm_projector_norm(weight: tuple[str, torch.Tensor]):
            return weight[0].startswith("pre_mm_projector_norm")

        # Get references to parameters for direct loading
        vision_encoder_dict = dict(self.vision_encoder.named_parameters())
        patch_merger_dict = dict(self.patch_merger.named_parameters(
        )) if self.vision_args.mm_projector_id == PATCH_MERGE else dict()
        pre_mm_projector_norm_dict = dict(
            self.pre_mm_projector_norm.named_parameters(
            )) if self.vision_args.add_pre_mm_projector_layer_norm else dict()
        vision_lang_adapter_dict = dict(
            self.vision_language_adapter.named_parameters())

        def llm_weights_generator():
            # Single pass over weights
            for name, w in weights:
                if is_vision_encoder_weights((name, w)):
                    # Load vision encoder weights directly
                    trimmed_name = '.'.join(name.split(".")[1:])
                    param = vision_encoder_dict[trimmed_name]
                    with torch.no_grad():
                        default_weight_loader(param, w)
                elif is_patch_merger((name, w)):
                    # Load vision patch merger weights directly
                    trimmed_name = '.'.join(name.split(".")[1:])
                    param = patch_merger_dict[trimmed_name]
                    with torch.no_grad():
                        default_weight_loader(param, w)
                elif is_pre_mm_projector_norm((name, w)):
                    # Load vision pre_mm_projector_norm weights directly
                    trimmed_name = '.'.join(name.split(".")[1:])
                    param = pre_mm_projector_norm_dict[trimmed_name]
                    with torch.no_grad():
                        default_weight_loader(param, w)
                elif is_vision_lang_adapter_weights((name, w)):
                    # Load vision-language adapter weights directly
                    trimmed_name = '.'.join(name.split(".")[1:])
                    param = vision_lang_adapter_dict[trimmed_name]
                    with torch.no_grad():
                        default_weight_loader(param, w)
                else:
                    # LLM weights: yield them to be loaded
                    # by language_model.load_weights
                    yield (name, w)

        # Now we call the language model load with the generator
        self.language_model.load_weights(llm_weights_generator())


# Vision encoder
@dataclass
class VisionEncoderArgs:
    hidden_size: int
    num_channels: int
    image_size: int
    patch_size: int
    intermediate_size: int
    num_hidden_layers: int
    num_attention_heads: int
    rope_theta: float  # for rope-2D
    image_token_id: int
    adapter_bias: bool = True
    spatial_merge_size: int = 1
    add_pre_mm_projector_layer_norm: bool = False
    mm_projector_id: str = ""


def _reshape_for_broadcast(freqs_cis: torch.Tensor,
                           x: torch.Tensor) -> torch.Tensor:
    """
    freqs_cis: complex - (seq_len, head_dim / 2)
    x: complex - (bsz, seq_len, head_dim / 2)
    """
    ndim = x.ndim
    assert ndim > 1
    assert freqs_cis.shape == (x.shape[1], x.shape[-1]), (
        freqs_cis.shape,
        (x.shape[1], x.shape[-1]),
    )
    shape = [
        d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)
    ]
    return freqs_cis.view(*shape)


def precompute_freqs_cis_2d(
    dim: int,
    height: int,
    width: int,
    theta: float,
) -> torch.Tensor:
    """
    freqs_cis: 2D complex tensor of shape (height, width, dim // 2)
        to be indexed by (height, width) position tuples
    """
    # (dim / 2) frequency bases
    freqs = 1.0 / (theta**(torch.arange(0, dim, 2).float() / dim))

    h = torch.arange(height, device=freqs.device)
    w = torch.arange(width, device=freqs.device)

    freqs_h = torch.outer(h, freqs[::2]).float()
    freqs_w = torch.outer(w, freqs[1::2]).float()
    freqs_2d = torch.cat(
        [
            freqs_h[:, None, :].repeat(1, width, 1),
            freqs_w[None, :, :].repeat(height, 1, 1),
        ],
        dim=-1,
    )
    return torch.polar(torch.ones_like(freqs_2d), freqs_2d)


def apply_rotary_emb_vit(
    xq: torch.Tensor,
    xk: torch.Tensor,
    freqs_cis: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
    xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
    xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
    assert freqs_cis.dtype == torch.complex64
    freqs_cis = _reshape_for_broadcast(freqs_cis, xq_)
    xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3)
    xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3)
    return xq_out.type_as(xq), xk_out.type_as(xk)


class FeedForward(nn.Module):

    def __init__(self, args: VisionEncoderArgs):
        super().__init__()
        assert args.intermediate_size is not None
        self.w1 = nn.Linear(args.hidden_size,
                            args.intermediate_size,
                            bias=False)
        self.w2 = nn.Linear(args.intermediate_size,
                            args.hidden_size,
                            bias=False)
        self.w3 = nn.Linear(args.hidden_size,
                            args.intermediate_size,
                            bias=False)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.w2(F.silu(self.w1(x)) * self.w3(x))


class Attention(nn.Module):

    def __init__(self, args: VisionEncoderArgs):
        super().__init__()
        self.args = args
        assert not args.hidden_size % args.num_attention_heads
        self.n_heads = args.num_attention_heads
        self.head_dim = args.hidden_size // args.num_attention_heads

        self.wq = nn.Linear(args.hidden_size, args.hidden_size, bias=False)
        self.wk = nn.Linear(args.hidden_size, args.hidden_size, bias=False)
        self.wv = nn.Linear(args.hidden_size, args.hidden_size, bias=False)
        self.wo = nn.Linear(args.hidden_size, args.hidden_size, bias=False)

    def forward(
        self,
        x: torch.Tensor,
        mask: torch.Tensor,
        freqs_cis: torch.Tensor,
    ) -> torch.Tensor:
        batch, patches, _ = x.shape

        q, k, v = self.wq(x), self.wk(x), self.wv(x)
        q = q.reshape(batch, patches, self.n_heads, self.head_dim)
        k = k.reshape(batch, patches, self.n_heads, self.head_dim)
        v = v.reshape(batch, patches, self.n_heads, self.head_dim)

        q, k = apply_rotary_emb_vit(q, k, freqs_cis=freqs_cis)

        if USE_XFORMERS_OPS:
            out = xops.memory_efficient_attention(q, k, v, attn_bias=mask)
        else:
            q = q.transpose(1, 2)
            k = k.transpose(1, 2)
            v = v.transpose(1, 2)
            out = nn.functional.scaled_dot_product_attention(q,
                                                             k,
                                                             v,
                                                             attn_mask=mask)
            out = out.transpose(1, 2)

        out = out.reshape(batch, patches, self.n_heads * self.head_dim)
        return self.wo(out)


class TransformerBlock(nn.Module):

    def __init__(self, args: VisionEncoderArgs):
        super().__init__()
        self.attention = Attention(args)
        self.feed_forward = FeedForward(args)
        self.attention_norm = RMSNorm(args.hidden_size, eps=1e-5)
        self.ffn_norm = RMSNorm(args.hidden_size, eps=1e-5)

    def forward(
        self,
        x: torch.Tensor,
        mask: torch.Tensor,
        freqs_cis: torch.Tensor,
    ) -> torch.Tensor:
        r = self.attention.forward(self.attention_norm(x),
                                   mask=mask,
                                   freqs_cis=freqs_cis)
        h = x + r
        r = self.feed_forward.forward(self.ffn_norm(h))
        out = h + r
        return out


class Transformer(nn.Module):

    def __init__(self, args: VisionEncoderArgs):
        super().__init__()
        self.layers = torch.nn.ModuleList()
        for _ in range(args.num_hidden_layers):
            self.layers.append(TransformerBlock(args))

    def forward(
        self,
        x: torch.Tensor,
        mask: torch.Tensor,
        freqs_cis: Optional[torch.Tensor],
    ) -> torch.Tensor:
        for layer in self.layers:
            x = layer(x, mask=mask, freqs_cis=freqs_cis)
        return x


def position_meshgrid(patch_embeds_list: list[torch.Tensor], ) -> torch.Tensor:
    positions = torch.cat([
        torch.stack(
            torch.meshgrid(
                torch.arange(p.shape[-2]),
                torch.arange(p.shape[-1]),
                indexing="ij",
            ),
            dim=-1,
        ).reshape(-1, 2) for p in patch_embeds_list
    ])
    return positions


class VisionTransformer(nn.Module):

    def __init__(self, args: VisionEncoderArgs):
        super().__init__()
        self.args = args
        self.patch_conv = nn.Conv2d(
            in_channels=args.num_channels,
            out_channels=args.hidden_size,
            kernel_size=args.patch_size,
            stride=args.patch_size,
            bias=False,
        )
        self.ln_pre = RMSNorm(args.hidden_size, eps=1e-5)
        self.transformer = Transformer(args)

        head_dim = self.args.hidden_size // self.args.num_attention_heads
        assert head_dim % 2 == 0, "ROPE requires even head_dim"
        self._freqs_cis: Optional[torch.Tensor] = None

    @property
    def max_patches_per_side(self) -> int:
        return self.args.image_size // self.args.patch_size

    @property
    def device(self) -> torch.types.Device:
        return next(self.parameters()).device

    @property
    def dtype(self) -> torch.dtype:
        return next(self.parameters()).dtype

    @property
    def freqs_cis(self) -> torch.Tensor:
        if self._freqs_cis is None:
            self._freqs_cis = precompute_freqs_cis_2d(
                dim=self.args.hidden_size // self.args.num_attention_heads,
                height=self.max_patches_per_side,
                width=self.max_patches_per_side,
                theta=self.args.rope_theta,
            )

        if self._freqs_cis.device != self.device:
            self._freqs_cis = self._freqs_cis.to(device=self.device)

        return self._freqs_cis

    def forward(
        self,
        images: list[torch.Tensor],
    ) -> torch.Tensor:
        """
        Args:
            images: list of N_img images of variable sizes,
                each of shape (C, H, W)
        Returns:
            image_features: tensor of token features for
                all tokens of all images of shape (N_toks, D)
        """
        # pass images through initial convolution independently
        patch_embeds_list = [
            self.patch_conv(img.unsqueeze(0).to(self.dtype)) for img in images
        ]

        patch_embeds = [
            p.flatten(2).permute(0, 2, 1) for p in patch_embeds_list
        ]
        embed_sizes = [p.shape[1] for p in patch_embeds]

        # flatten to a single sequence
        patch_embeds = torch.cat(patch_embeds, dim=1)
        patch_embeds = self.ln_pre(patch_embeds)

        # positional embeddings
        positions = position_meshgrid(patch_embeds_list).to(self.device)
        freqs_cis = self.freqs_cis[positions[:, 0], positions[:, 1]]

        # pass through Transformer with a block diagonal mask delimiting images
        if USE_XFORMERS_OPS:
            mask = xops.fmha.attn_bias.BlockDiagonalMask.from_seqlens(
                [p.shape[-2] * p.shape[-1] for p in patch_embeds_list], )
        else:
            from transformers.models.pixtral.modeling_pixtral import (
                generate_block_attention_mask)
            mask = generate_block_attention_mask(
                [p.shape[-2] * p.shape[-1] for p in patch_embeds_list],
                patch_embeds)
        out = self.transformer(patch_embeds, mask=mask, freqs_cis=freqs_cis)

        # squeeze dim 0 and split into separate tensors for each image
        return torch.split(out.squeeze(0), embed_sizes)


class VisionLanguageAdapter(nn.Module):

    def __init__(self, args: VisionEncoderArgs, dim: int):
        super().__init__()
        assert isinstance(args, VisionEncoderArgs)
        self.w_in = nn.Linear(
            args.hidden_size,
            dim,
            bias=args.adapter_bias,
        )
        self.gelu = nn.GELU()
        self.w_out = nn.Linear(dim, dim, bias=args.adapter_bias)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.w_out(self.gelu(self.w_in(x)))


class PatchMerger(nn.Module):
    """
    Learned merging of spatial_merge_size ** 2 patches
    """

    def __init__(
        self,
        vision_encoder_dim: int,
        spatial_merge_size: int,
        use_mlp_bias: bool = False,
    ) -> None:
        super().__init__()

        mlp_input_dim = vision_encoder_dim * (spatial_merge_size**2)

        self.spatial_merge_size = spatial_merge_size
        self.mlp_input_dim = mlp_input_dim

        self.merging_layer = nn.Linear(
            mlp_input_dim,
            vision_encoder_dim,
            bias=use_mlp_bias,
        )

    def forward(self, x: torch.Tensor,
                image_sizes: list[tuple[int, int]]) -> torch.Tensor:
        # image_sizes specified in tokens
        assert sum([h * w for h, w in image_sizes]) == len(x)

        # x is (N, vision_encoder_dim)
        x = self.permute(x, image_sizes)

        # x is (N / spatial_merge_size ** 2,
        #       vision_encoder_dim * spatial_merge_size ** 2)
        x = self.merging_layer(x)

        # x is (N / spatial_merge_size ** 2, vision_encoder_dim)
        return x

    def permute(
        self,
        x: torch.Tensor,
        image_sizes: list[tuple[int, int]],
    ) -> torch.Tensor:
        """
        Args:
            x: (N, D) where N is flattened and concatenated patch tokens
                for all images
            image_sizes: list of tuple of (height, width) in tokens for
                each image
        Returns:
            image_features: reorders patch tokens so each grid of
                (spatial_merge_size, spatial_merge_size) is contiguous.
                now (N / spatial_merge_size ** 2, D * spatial_merge_size ** 2)
        """

        sub_grids = get_sub_grids(
            x=x,
            image_sizes=image_sizes,
            spatial_merge_size=self.spatial_merge_size
        )  # list of [d x sub_grid_size x sub_grid_size x n_patches]
        permuted_tensor: list[torch.Tensor] = []
        for grid in sub_grids:
            n_patches = grid.shape[-1]
            permuted_tensor.append(grid.view(-1, n_patches).t(
            ))  # n_patches x d * sub_grid_size * sub_grid_size
        return torch.cat(
            permuted_tensor, dim=0
        )  # (N / spatial_merge_size ** 2, d * spatial_merge_size ** 2)


def get_sub_grids(
    x: torch.Tensor,
    image_sizes: list[tuple[int, int]],
    spatial_merge_size: int,
) -> list[torch.Tensor]:
    # image_sizes specified in tokens
    tokens_per_image = [h * w for h, w in image_sizes]
    d = x.shape[-1]
    all_img_sub_grids: list[torch.Tensor] = []
    sub_grid_size = spatial_merge_size

    for image_index, image_tokens in enumerate(x.split(tokens_per_image)):
        # Reshape image_tokens into a 2D grid
        h, w = image_sizes[image_index]
        image_grid = image_tokens.view(h, w, d).permute(
            2, 0, 1)[None, :, :, :]  # 1 x d x h x w
        sub_grids = torch.nn.functional.unfold(image_grid,
                                               kernel_size=sub_grid_size,
                                               stride=sub_grid_size)
        sub_grids = sub_grids.view(
            1, d, sub_grid_size, sub_grid_size,
            -1)  # 1 x d x sub_grid_size x sub_grid_size x n_patches

        all_img_sub_grids.append(sub_grids[0])

    return all_img_sub_grids


#### HF Transformers version of Pixtral ####
# Based off https://github.com/huggingface/transformers/blob/d7950bff82b18c823193d17d72188c5e46d06c83/src/transformers/models/pixtral/modeling_pixtral.py
# This model follows the Llava family, meaning image embeddings are placed
# instead of the `[IMG]` token placeholders.
# The model uses [`PixtralVisionModel`] for its vision encoder,
# and [`MistralForCausalLM`] for its language decoder.


class PixtralHFEncoderInfo(VisionEncoderInfo[PixtralVisionConfig]):

    def get_num_image_tokens(
        self,
        *,
        image_width: int,
        image_height: int,
    ) -> int:
        ncols, nrows = self.get_patch_grid_size(
            image_width=image_width,
            image_height=image_height,
        )
        return ncols * nrows

    def get_image_size(self) -> int:
        return self.vision_config.image_size

    def get_patch_size(self) -> int:
        # spatial_merge_size is needed for Mistral3
        spatial_merge_size = getattr(self.hf_config, "spatial_merge_size", 1)
        return self.vision_config.patch_size * spatial_merge_size

    def get_patch_grid_length(self) -> int:
        image_size, patch_size = self.get_image_size(), self.get_patch_size()

        # Since interpolation is applied, the image size need not be divisible
        # assert image_size % patch_size == 0
        return image_size // patch_size

    # Adapted from: https://github.com/huggingface/transformers/blob/v4.49.0/src/transformers/models/pixtral/image_processing_pixtral.py#L99
    def get_patch_grid_size(
        self,
        *,
        image_width: int,
        image_height: int,
    ) -> tuple[int, int]:
        max_width = max_height = self.get_image_size()
        patch_width = patch_height = self.get_patch_size()

        ratio = max(image_width / max_width, image_height / max_height)

        if ratio > 1:
            image_width = int(math.floor(image_width / ratio))
            image_height = int(math.floor(image_height / ratio))

        nrows, ncols = _get_pixtral_hf_num_image_tokens(
            (image_height, image_width),
            (patch_height, patch_width),
        )  # type: ignore

        return ncols, nrows


class PixtralHFMLP(nn.Module):

    def __init__(
        self,
        config: PixtralVisionConfig,
        quant_config: Optional[QuantizationConfig] = None,
        *,
        prefix: str = "",
    ) -> None:
        super().__init__()

        assert config.intermediate_size is not None
        self.gate_up_proj = MergedColumnParallelLinear(
            input_size=config.hidden_size,
            output_sizes=[config.intermediate_size] * 2,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.gate_up_proj")
        self.down_proj = RowParallelLinear(input_size=config.intermediate_size,
                                           output_size=config.hidden_size,
                                           bias=False,
                                           quant_config=quant_config,
                                           prefix=f"{prefix}.down_proj")
        self.act_and_mul = get_act_and_mul_fn(config.hidden_act)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        gate_up, _ = self.gate_up_proj(x)
        x = self.act_and_mul(gate_up)
        x, _ = self.down_proj(x)
        return x


class PixtralHFAttention(nn.Module):

    def __init__(
        self,
        config: PixtralVisionConfig,
        quant_config: Optional[QuantizationConfig] = None,
        *,
        prefix: str = "",
    ) -> None:
        super().__init__()

        self.config = config
        assert not config.hidden_size % config.num_attention_heads
        self.total_num_heads = config.num_attention_heads
        tp_size = get_tensor_model_parallel_world_size()
        self.n_heads = divide(config.num_attention_heads, tp_size)
        self.head_dim = config.hidden_size // config.num_attention_heads

        self.qkv_proj = QKVParallelLinear(
            hidden_size=config.hidden_size,
            head_size=self.head_dim,
            total_num_heads=self.total_num_heads,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.qkv_proj",
        )
        assert self.total_num_heads * self.head_dim == config.hidden_size
        self.o_proj = RowParallelLinear(
            input_size=config.hidden_size,
            output_size=config.hidden_size,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.o_proj",
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor,
        position_embeddings: torch.Tensor,
    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
        batch, patches, _ = hidden_states.size()

        qkv_states, _ = self.qkv_proj(hidden_states)
        q, k, v = qkv_states.chunk(3, dim=-1)

        # Transpose q and k to apply HF's Rotary Position Embedding
        q = q.view(batch, patches, self.n_heads, self.head_dim).transpose(1, 2)
        k = k.view(batch, patches, self.n_heads, self.head_dim).transpose(1, 2)
        v = v.view(batch, patches, self.n_heads, self.head_dim)
        cos, sin = position_embeddings
        q, k = apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=0)

        if USE_XFORMERS_OPS:
            # Transpose q and k back for attention
            q = q.transpose(1, 2).contiguous()
            k = k.transpose(1, 2).contiguous()
            out = xops.memory_efficient_attention(q,
                                                  k,
                                                  v,
                                                  attn_bias=attention_mask)
        else:
            v = v.transpose(1, 2)
            out = nn.functional.scaled_dot_product_attention(
                q, k, v, attn_mask=attention_mask)
            out = out.transpose(1, 2)

        out = out.view(batch, patches, self.n_heads * self.head_dim)
        attn_output, _ = self.o_proj(out)

        return attn_output, None


class PixtralHFTransformerBlock(nn.Module):

    def __init__(
        self,
        config: PixtralVisionConfig,
        quant_config: Optional[QuantizationConfig] = None,
        *,
        prefix: str = "",
    ) -> None:
        super().__init__()

        self.attention_norm = RMSNorm(config.hidden_size, eps=1e-5)
        self.attention = PixtralHFAttention(config,
                                            quant_config=quant_config,
                                            prefix=f"{prefix}.attention")
        self.feed_forward = PixtralHFMLP(config,
                                         quant_config=quant_config,
                                         prefix=f"{prefix}.feed_forward")
        self.ffn_norm = RMSNorm(config.hidden_size, eps=1e-5)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor,
        position_embeddings: torch.Tensor,
    ) -> torch.Tensor:
        r, _ = self.attention.forward(self.attention_norm(hidden_states),
                                      attention_mask=attention_mask,
                                      position_embeddings=position_embeddings)
        h = hidden_states + r
        r = self.feed_forward.forward(self.ffn_norm(h))
        out = h + r
        return out


class PixtralHFTransformer(nn.Module):

    def __init__(
        self,
        config: PixtralVisionConfig,
        quant_config: Optional[QuantizationConfig] = None,
        *,
        num_hidden_layers_override: Optional[int] = None,
        prefix: str = "",
    ) -> None:
        super().__init__()

        if num_hidden_layers_override is None:
            num_hidden_layers = config.num_hidden_layers
        else:
            num_hidden_layers = num_hidden_layers_override

        self.layers = nn.ModuleList([
            PixtralHFTransformerBlock(config=config,
                                      quant_config=quant_config,
                                      prefix=f"{prefix}.layers.{layer_idx}")
            for layer_idx in range(num_hidden_layers)
        ])

    def forward(
        self,
        x: torch.Tensor,
        attention_mask: torch.Tensor,
        position_embeddings: torch.Tensor,
        return_all_hidden_states: bool,
    ) -> torch.Tensor:
        hidden_states_pool = [x]

        for layer in self.layers:
            x = layer(x, attention_mask, position_embeddings)
            if return_all_hidden_states:
                hidden_states_pool.append(x)
        # If we have multiple feature sample layers, we return all hidden
        # states in order and grab the ones we need by index.
        if return_all_hidden_states:
            return hidden_states_pool
        return x


class PixtralHFVisionModel(nn.Module):

    def __init__(
        self,
        config: PixtralVisionConfig,
        quant_config: Optional[QuantizationConfig] = None,
        *,
        num_hidden_layers_override: Optional[int] = None,
        require_post_norm: Optional[bool] = None,
        prefix: str = "",
    ) -> None:
        super().__init__()

        self.config = config

        self.patch_conv = nn.Conv2d(
            in_channels=config.num_channels,
            out_channels=config.hidden_size,
            kernel_size=config.patch_size,
            stride=config.patch_size,
            bias=False,
        )
        self.ln_pre = RMSNorm(config.hidden_size, eps=1e-5)
        self.transformer = PixtralHFTransformer(
            config,
            quant_config,
            num_hidden_layers_override=num_hidden_layers_override,
            prefix=f"{prefix}.transformer",
        )

        num_hidden_layers = config.num_hidden_layers
        if len(self.transformer.layers) > config.num_hidden_layers:
            raise ValueError(
                f"The original encoder only has {num_hidden_layers} "
                f"layers, but you requested {len(self.transformer.layers)} "
                "layers.")

        if require_post_norm is True:
            msg = "PixtralHFVisionModel does not have post-layernorm"
            raise ValueError(msg)

        self.dtype = next(self.parameters()).dtype
        self.device = next(self.parameters()).device
        self.patch_positional_embedding = PixtralRotaryEmbedding(
            config, self.device)

    def forward(
        self,
        pixel_values: list[torch.Tensor],
        feature_sample_layers: Optional[list[int]] = None,
    ) -> tuple[torch.Tensor, ...]:
        """
        Args:
            pixel_values: Each image to be processed will be a separate tensor
                in pixel_values. This means it will be a list of tensors
                because multiple requests batched can have multiple images,
                each with their own shape potentially
            feature_sample_layers: Layer indices whose features should be
                concatenated and used as the visual encoder output. If none
                are provided, the last layer is used.

        Returns:
            image_features: tensor of token features for
                all tokens of all images of shape (N_toks, D)
        """
        # pass images through initial convolution independently
        patch_embeds_list = [
            self.patch_conv(img.unsqueeze(0).to(self.dtype))
            for img in pixel_values
        ]

        patch_embeds = [
            p.flatten(2).permute(0, 2, 1) for p in patch_embeds_list
        ]
        embed_sizes = [p.shape[1] for p in patch_embeds]

        # flatten to a single sequence
        patch_embeds = torch.cat(patch_embeds, dim=1)
        patch_embeds = self.ln_pre(patch_embeds)

        # positional embeddings
        position_ids = position_ids_in_meshgrid(
            patch_embeds_list,
            max_width=self.config.image_size // self.config.patch_size).to(
                self.device)
        position_embedding = self.patch_positional_embedding(
            patch_embeds, position_ids)

        if USE_XFORMERS_OPS:
            attention_mask = xops.fmha.attn_bias.BlockDiagonalMask.from_seqlens(
                [p.shape[-2] * p.shape[-1] for p in patch_embeds_list], )
        else:
            from transformers.models.pixtral.modeling_pixtral import (
                generate_block_attention_mask)
            attention_mask = generate_block_attention_mask(
                [p.shape[-2] * p.shape[-1] for p in patch_embeds_list],
                patch_embeds)

        return_all_hidden_states = feature_sample_layers is not None
        out = self.transformer(
            patch_embeds,
            attention_mask,
            position_embedding,
            return_all_hidden_states=return_all_hidden_states)

        out = resolve_visual_encoder_outputs(out, feature_sample_layers, None,
                                             self.config.num_hidden_layers)

        # squeeze dim 0 and split into separate tensors for each image
        return torch.split(out.squeeze(0), embed_sizes)

    # (TODO) Add prefix argument for filtering out weights to be loaded
    #        ref: https://github.com/vllm-project/vllm/pull/7186#discussion_r1734163986
    def load_weights(self, weights: Iterable[tuple[str,
                                                   torch.Tensor]]) -> set[str]:
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            (".qkv_proj", ".q_proj", "q"),
            (".qkv_proj", ".k_proj", "k"),
            (".qkv_proj", ".v_proj", "v"),
            (".gate_up_proj", ".gate_proj", 0),
            (".gate_up_proj", ".up_proj", 1),
        ]
        params_dict = dict(self.named_parameters())
        loaded_params: set[str] = set()
        layer_count = len(self.transformer.layers)

        for name, loaded_weight in weights:
            # omit layers when num_hidden_layers_override is set
            if name.startswith("transformer.layers"):
                layer_idx = int(name.split(".")[2])
                if layer_idx >= layer_count:
                    continue

            for (param_name, weight_name, shard_id) in stacked_params_mapping:
                if weight_name not in name:
                    continue
                name = name.replace(weight_name, param_name)
                param = params_dict[name]
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader",
                                        default_weight_loader)
                weight_loader(param, loaded_weight)
            loaded_params.add(name)
        return loaded_params
